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Related Experiment Video

Updated: Jun 28, 2026

Quantification of Orofacial Phenotypes in Xenopus

Quantification of Orofacial Phenotypes in Xenopus

Published on: November 6, 2014

Performing procrustes discriminant analysis with HOLMES.

D González-Arjona¹, G López-Pérez, A G González

¹Department of Physical Chemistry, University of Seville, 41012 Seville, Spain.

|October 31, 2008

Summary

Program HOLMES was updated for procrustes discriminant analysis (PDA), a method equivalent to partial least squares-discriminant analysis (PLS-DA). This enhanced program is demonstrated through literature case studies for improved data analysis.

Related Experiment Videos

Last Updated: Jun 28, 2026

Quantification of Orofacial Phenotypes in Xenopus

Quantification of Orofacial Phenotypes in Xenopus

Published on: November 6, 2014

Area of Science:

Chemometrics
Multivariate data analysis
Statistical pattern recognition

Background:

Target factor analysis is a common technique for analyzing complex datasets.
Procrustes discriminant analysis (PDA) offers a robust method for classification.
Existing computational methods for PDA require updates for broader applicability.

Purpose of the Study:

To update the HOLMES program for enhanced procrustes discriminant analysis (PDA).
To establish the mathematical equivalence between PDA and partial least squares-discriminant analysis (PLS-DA).
To demonstrate the practical application of the updated PDA through case studies.

Main Methods:

Updating the HOLMES program incorporating computational details for PDA.
Establishing theoretical equivalence between PDA and PLS-DA.
Applying the updated PDA to two real-world datasets from existing literature.

Main Results:

The HOLMES program has been successfully updated for PDA.
A clear mathematical equivalence between PDA and PLS-DA was demonstrated.
The updated PDA method showed effective application in the analyzed case studies.

Conclusions:

The updated HOLMES program provides an effective tool for procrustes discriminant analysis.
The established equivalence with PLS-DA broadens the accessibility and understanding of PDA.
The demonstrated applications highlight the utility of PDA in multivariate classification problems.